{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:07:19.740298Z",
     "start_time": "2024-09-26T03:07:18.212473Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ],
   "id": "cdd5b20dfbd8fa5d",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Duplicate key in file WindowsPath('D:/Anaconda3/Lib/site-packages/matplotlib/mpl-data/matplotlibrc'), line 271 ('font.sans-serif: SimHei')\n",
      "Duplicate key in file WindowsPath('D:/Anaconda3/Lib/site-packages/matplotlib/mpl-data/matplotlibrc'), line 414 ('axes.unicode_minus: False  # use Unicode for the minus symbol rather than hyphen.  See')\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-09-26T02:57:46.094858Z",
     "start_time": "2024-09-26T02:57:45.304416Z"
    }
   },
   "source": [
    "#训练集 \n",
    "master_train = pd.read_csv(r\"D:/桌面/魔镜杯风控算法大赛/数据/PPD-First-Round-Data-Update/Training_Set/PPD_Training_Master_GBK_3_1_Training_Set.csv\",encoding='gbk')"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T02:58:03.088517Z",
     "start_time": "2024-09-26T02:58:03.084408Z"
    }
   },
   "cell_type": "code",
   "source": "master_train.shape",
   "id": "ae9f93c23c3c6708",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 228)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:00:30.976105Z",
     "start_time": "2024-09-26T03:00:30.697204Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#测试集\n",
    "master_test = pd.read_csv(r\"D:/桌面/魔镜杯风控算法大赛/数据/PPD-First-Round-Data-Update/Test_Set/PPD_Master_GBK_2_Test_Set.csv\",encoding='gbk', encoding_errors='ignore')"
   ],
   "id": "d8d4a95b5c726aa0",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:00:32.067316Z",
     "start_time": "2024-09-26T03:00:32.063323Z"
    }
   },
   "cell_type": "code",
   "source": "master_test.shape",
   "id": "1e740469000159ad",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19999, 227)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "训练集数据分析",
   "id": "debe67d12de40677"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:01:32.360058Z",
     "start_time": "2024-09-26T03:01:32.354028Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#标签不平衡 1 = 贷款违约，0 = 正常还款\n",
    "master_train['target'].value_counts()"
   ],
   "id": "4fa2dee5aa42165c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       "0    27802\n",
       "1     2198\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:03:43.073229Z",
     "start_time": "2024-09-26T03:03:43.063858Z"
    }
   },
   "cell_type": "code",
   "source": "master_train.info()",
   "id": "ee2055bca4dbecc9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30000 entries, 0 to 29999\n",
      "Columns: 228 entries, Idx to ListingInfo\n",
      "dtypes: float64(38), int64(170), object(20)\n",
      "memory usage: 52.2+ MB\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:07:06.529501Z",
     "start_time": "2024-09-26T03:07:06.502134Z"
    }
   },
   "cell_type": "code",
   "source": [
    "missing_values = master_train.isnull().sum() \n",
    "missing_values"
   ],
   "id": "bbc468ca960b4562",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Idx                   0\n",
       "UserInfo_1            6\n",
       "UserInfo_2          302\n",
       "UserInfo_3            7\n",
       "UserInfo_4          268\n",
       "                   ... \n",
       "SocialNetwork_15      0\n",
       "SocialNetwork_16      0\n",
       "SocialNetwork_17      0\n",
       "target                0\n",
       "ListingInfo           0\n",
       "Length: 228, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:07:39.347972Z",
     "start_time": "2024-09-26T03:07:38.969679Z"
    }
   },
   "cell_type": "code",
   "source": [
    "missing_values = missing_values[missing_values > 0]\n",
    "missing_values = missing_values.sort_values(ascending=False)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=missing_values.index, y=missing_values.values)\n",
    "plt.title('Missing Values Count per Feature')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Number of Missing Values')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ],
   "id": "300c42f620817cf3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "发现webinfo_3 和 webinfo_1 缺失值数量高达97% 直接删掉 UserInfo_11、UserInfo_12 和 UserInfo_13 的缺失值比率为 63%，这三列属\n",
    "性是类别型的，可以将缺失值用-1 填充，相当于“是否缺失”当成另一种类别。"
   ],
   "id": "3c06f67cf777f955"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:18:12.703933Z",
     "start_time": "2024-09-26T03:18:12.668353Z"
    }
   },
   "cell_type": "code",
   "source": [
    "master_train = master_train.drop(['WeblogInfo_3'],axis=1)\n",
    "master_train = master_train.drop(['WeblogInfo_1'],axis=1)"
   ],
   "id": "79f5693d3a3fe579",
   "outputs": [],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:18:14.980681Z",
     "start_time": "2024-09-26T03:18:14.977395Z"
    }
   },
   "cell_type": "code",
   "source": "master_train.shape",
   "id": "adb82de9694e9dba",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30000, 226)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:23:32.005444Z",
     "start_time": "2024-09-26T03:23:31.998091Z"
    }
   },
   "cell_type": "code",
   "source": [
    "master_train['UserInfo_11'].fillna('-1',inplace=True)\n",
    "master_train['UserInfo_12'].fillna('-1',inplace=True)\n",
    "master_train['UserInfo_13'].fillna('-1',inplace=True)"
   ],
   "id": "f0bf8e377e68f292",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jiabaosen\\AppData\\Local\\Temp\\ipykernel_22324\\2164529792.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  master_train['UserInfo_12'].fillna('-1',inplace=True)\n",
      "C:\\Users\\jiabaosen\\AppData\\Local\\Temp\\ipykernel_22324\\2164529792.py:2: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '-1' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
      "  master_train['UserInfo_12'].fillna('-1',inplace=True)\n",
      "C:\\Users\\jiabaosen\\AppData\\Local\\Temp\\ipykernel_22324\\2164529792.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  master_train['UserInfo_13'].fillna('-1',inplace=True)\n",
      "C:\\Users\\jiabaosen\\AppData\\Local\\Temp\\ipykernel_22324\\2164529792.py:3: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '-1' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
      "  master_train['UserInfo_13'].fillna('-1',inplace=True)\n"
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:24:21.093050Z",
     "start_time": "2024-09-26T03:24:20.826302Z"
    }
   },
   "cell_type": "code",
   "source": [
    "missing_values = master_train.isnull().sum() \n",
    "missing_values = missing_values[missing_values > 0]\n",
    "missing_values = missing_values.sort_values(ascending=False)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=missing_values.index, y=missing_values.values)\n",
    "plt.title('Missing Values Count per Feature')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Number of Missing Values')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ],
   "id": "59de159436236fcb",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "缺失值分布情况 下面分来分析数据缺失值情况",
   "id": "52399b3355ec1eda"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:55:49.363987Z",
     "start_time": "2024-09-26T03:55:45.663029Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 创建一个 6x3 的 subplot\n",
    "fig, axs = plt.subplots(6,3, figsize=(15, 10))\n",
    "\n",
    "# 列名列表\n",
    "columns = ['WeblogInfo_20', 'WeblogInfo_21', 'WeblogInfo_19', \n",
    "           'WeblogInfo_2', 'WeblogInfo_4', 'WeblogInfo_6',\n",
    "           'UserInfo_2', 'UserInfo_4', 'WeblogInfo_31',\n",
    "           'WeblogInfo_42', 'WeblogInfo_38', 'WeblogInfo_39',\n",
    "           'WeblogInfo_40', 'WeblogInfo_41', 'WeblogInfo_47',\n",
    "           'WeblogInfo_43', 'WeblogInfo_44', 'WeblogInfo_45',\n",
    "           ]\n",
    "\n",
    "# 遍历列并绘制直方图\n",
    "for ax, col in zip(axs.flatten(), columns):\n",
    "    value_counts = master_train[col].value_counts()\n",
    "    ax.bar(value_counts.index, value_counts.values, color='skyblue')\n",
    "    ax.set_title(f'Value Counts of {col}')\n",
    "    ax.set_xlabel('Unique Values')\n",
    "    ax.set_ylabel('Counts')\n",
    "    ax.set_xticks(value_counts.index)\n",
    "\n",
    "# 调整布局\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "3075073fe9e8a768",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1000 with 18 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 55
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "User_Info_2  和 User_Info_4 属于 市  ",
   "id": "c6597665670a6b82"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "",
   "id": "f5403983a7f03bef"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "4715a4bd240fc5db"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:56:02.639409Z",
     "start_time": "2024-09-26T03:56:01.218163Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 创建一个 7x3 的 subplot\n",
    "fig, axs = plt.subplots(7,3, figsize=(15, 10))\n",
    "\n",
    "# 列名列表\n",
    "columns = [\n",
    "           'WeblogInfo_46', 'WeblogInfo_36', 'WeblogInfo_48',\n",
    "           'WeblogInfo_37', 'WeblogInfo_49', 'WeblogInfo_35',\n",
    "           'WeblogInfo_34', 'WeblogInfo_33', 'WeblogInfo_32',\n",
    "           'WeblogInfo_30', 'WeblogInfo_29', 'WeblogInfo_28',\n",
    "           'WeblogInfo_27', 'WeblogInfo_26', 'WeblogInfo_25',\n",
    "           'WeblogInfo_27', 'WeblogInfo_26', 'WeblogInfo_25',\n",
    "           'WeblogInfo_24', \n",
    "           ]\n",
    "\n",
    "# 遍历列并绘制直方图\n",
    "for ax, col in zip(axs.flatten(), columns):\n",
    "    value_counts = master_train[col].value_counts()\n",
    "    ax.bar(value_counts.index, value_counts.values, color='skyblue')\n",
    "    ax.set_title(f'Value Counts of {col}')\n",
    "    ax.set_xlabel('Unique Values')\n",
    "    ax.set_ylabel('Counts')\n",
    "    ax.set_xticks(value_counts.index)\n",
    "\n",
    "# 调整布局\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "7d3a43b261dd2f65",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1000 with 21 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 56
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:57:13.591358Z",
     "start_time": "2024-09-26T03:57:13.584875Z"
    }
   },
   "cell_type": "code",
   "source": [
    "master_train['WeblogInfo_20'].value_counts()\n",
    "master_train['WeblogInfo_49'].value_counts()"
   ],
   "id": "a9d25fab9f5a33f3",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WeblogInfo_49\n",
       "0.0    29747\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 57
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T03:59:49.904977Z",
     "start_time": "2024-09-26T03:59:49.896473Z"
    }
   },
   "cell_type": "code",
   "source": [
    "master_train['UserInfo_2'].value_counts()\n",
    "master_train['UserInfo_4'].value_counts()"
   ],
   "id": "7febb0c8cb2ecf0f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UserInfo_4\n",
       "深圳         844\n",
       "广州         806\n",
       "北京         683\n",
       "上海         675\n",
       "重庆         549\n",
       "          ... \n",
       "甘南藏族自治州      1\n",
       "那曲           1\n",
       "山南           1\n",
       "日喀则          1\n",
       "海南藏族自治州      1\n",
       "Name: count, Length: 330, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 58
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T12:10:39.219045Z",
     "start_time": "2024-09-26T12:10:39.202078Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#49都是0 删除 \n",
    "master_train = master_train.drop('WeblogInfo_49',axis=1)"
   ],
   "id": "f6388f8aef362a80",
   "outputs": [],
   "execution_count": 61
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T12:12:09.797357Z",
     "start_time": "2024-09-26T12:12:09.764201Z"
    }
   },
   "cell_type": "code",
   "source": "master_train.isnull().sum()",
   "id": "966f5a046ddc1075",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Idx                   0\n",
       "UserInfo_1            6\n",
       "UserInfo_2          302\n",
       "UserInfo_3            7\n",
       "UserInfo_4          268\n",
       "                   ... \n",
       "SocialNetwork_15      0\n",
       "SocialNetwork_16      0\n",
       "SocialNetwork_17      0\n",
       "target                0\n",
       "ListingInfo           0\n",
       "Length: 225, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 62
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T12:13:34.115237Z",
     "start_time": "2024-09-26T12:13:34.042770Z"
    }
   },
   "cell_type": "code",
   "source": "master_train.fillna('-1',inplace=True)",
   "id": "82adf6baed0f0a94",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jiabaosen\\AppData\\Local\\Temp\\ipykernel_22324\\3855372221.py:1: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. Value '-1' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
      "  master_train.fillna('-1',inplace=True)\n"
     ]
    }
   ],
   "execution_count": 63
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T12:55:31.285383Z",
     "start_time": "2024-09-26T12:55:31.212207Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 假设 df 是你的 DataFrame\n",
    "columns_to_convert = [\n",
    "    'UserInfo_1', 'UserInfo_2', 'UserInfo_3', 'UserInfo_4',\n",
    "    'WeblogInfo_2', 'WeblogInfo_4', 'WeblogInfo_5', 'WeblogInfo_6',\n",
    "    'UserInfo_7', 'UserInfo_8', 'UserInfo_9', 'UserInfo_11',\n",
    "    'UserInfo_12', 'UserInfo_13', 'UserInfo_19', 'UserInfo_20',\n",
    "    'UserInfo_22', 'UserInfo_23', 'UserInfo_24', 'Education_Info2',\n",
    "    'Education_Info3', 'Education_Info4', 'Education_Info6',\n",
    "    'Education_Info7', 'Education_Info8', 'WeblogInfo_19',\n",
    "    'WeblogInfo_20', 'WeblogInfo_21', 'WeblogInfo_23',\n",
    "    'WeblogInfo_24', 'WeblogInfo_25', 'WeblogInfo_26',\n",
    "    'WeblogInfo_27', 'WeblogInfo_28', 'WeblogInfo_29',\n",
    "    'WeblogInfo_30', 'WeblogInfo_31', 'WeblogInfo_32',\n",
    "    'WeblogInfo_33', 'WeblogInfo_34', 'WeblogInfo_35',\n",
    "    'WeblogInfo_36', 'WeblogInfo_37', 'WeblogInfo_38',\n",
    "    'WeblogInfo_39', 'WeblogInfo_40', 'WeblogInfo_41',\n",
    "    'WeblogInfo_42', 'WeblogInfo_43', 'WeblogInfo_44',\n",
    "    'WeblogInfo_45', 'WeblogInfo_46', 'WeblogInfo_47',\n",
    "    'WeblogInfo_48', 'ListingInfo'\n",
    "]\n",
    "\n",
    "# 将指定的列转换为字符串类型\n",
    "master_train[columns_to_convert] = master_train[columns_to_convert].astype(str)\n",
    "\n",
    "# 检查数据类型\n",
    "print(master_train.dtypes)"
   ],
   "id": "abf875ceedb076a8",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Idx                  int64\n",
      "UserInfo_1          object\n",
      "UserInfo_2          object\n",
      "UserInfo_3          object\n",
      "UserInfo_4          object\n",
      "                     ...  \n",
      "SocialNetwork_15     int64\n",
      "SocialNetwork_16     int64\n",
      "SocialNetwork_17     int64\n",
      "target               int64\n",
      "ListingInfo         object\n",
      "Length: 225, dtype: object\n"
     ]
    }
   ],
   "execution_count": 79
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "模型构建",
   "id": "6d34ae08843e9223"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T12:13:48.282216Z",
     "start_time": "2024-09-26T12:13:48.233623Z"
    }
   },
   "cell_type": "code",
   "source": "master_train.isnull().sum()",
   "id": "a8c94c8d3a1a455b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Idx                 0\n",
       "UserInfo_1          0\n",
       "UserInfo_2          0\n",
       "UserInfo_3          0\n",
       "UserInfo_4          0\n",
       "                   ..\n",
       "SocialNetwork_15    0\n",
       "SocialNetwork_16    0\n",
       "SocialNetwork_17    0\n",
       "target              0\n",
       "ListingInfo         0\n",
       "Length: 225, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 64
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": " ",
   "id": "f727ae09a49efb12"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "模型构建",
   "id": "de947949a7e4d5e8"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T12:54:58.902184Z",
     "start_time": "2024-09-26T12:54:58.796453Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#模型构建 \n",
    "from sklearn.decomposition import  PCA \n",
    "from sklearn.model_selection import train_test_split\n",
    "train = master_train.drop(columns=['target'])\n",
    "label = master_train['target']\n",
    "X_train, X_test, y_train, y_test = train_test_split( train, label, test_size=0.33, random_state=42) \n"
   ],
   "id": "8b7e0549011c058d",
   "outputs": [],
   "execution_count": 75
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T14:27:28.110665Z",
     "start_time": "2024-09-26T14:27:28.048981Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "5201120b787cb1e1",
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "('UserInfo_2', 'UserInfo_4', 'UserInfo_7', 'UserInfo_8', 'UserInfo_9', 'UserInfo_19', 'UserInfo_20', 'UserInfo_22', 'UserInfo_24', 'Education_Info3')",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   3804\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 3805\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_engine\u001B[38;5;241m.\u001B[39mget_loc(casted_key)\n\u001B[0;32m   3806\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m err:\n",
      "File \u001B[1;32mindex.pyx:167\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32mindex.pyx:196\u001B[0m, in \u001B[0;36mpandas._libs.index.IndexEngine.get_loc\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7081\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n",
      "File \u001B[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7089\u001B[0m, in \u001B[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001B[1;34m()\u001B[0m\n",
      "\u001B[1;31mKeyError\u001B[0m: ('UserInfo_2', 'UserInfo_4', 'UserInfo_7', 'UserInfo_8', 'UserInfo_9', 'UserInfo_19', 'UserInfo_20', 'UserInfo_22', 'UserInfo_24', 'Education_Info3')",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[85], line 3\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01msklearn\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m preprocessing\n\u001B[0;32m      2\u001B[0m le \u001B[38;5;241m=\u001B[39m preprocessing\u001B[38;5;241m.\u001B[39mLabelEncoder()\n\u001B[1;32m----> 3\u001B[0m X_train[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_2\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_4\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_7\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_8\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_9\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_19\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_20\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_22\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_24\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mEducation_Info3\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m le\u001B[38;5;241m.\u001B[39mfit_transform(X_train[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_2\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_4\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_7\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_8\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_9\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_19\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_20\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_22\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mUserInfo_24\u001B[39m\u001B[38;5;124m'\u001B[39m,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mEducation_Info3\u001B[39m\u001B[38;5;124m'\u001B[39m]) \n\u001B[0;32m      4\u001B[0m X_train\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:4102\u001B[0m, in \u001B[0;36mDataFrame.__getitem__\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   4100\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mnlevels \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m   4101\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_getitem_multilevel(key)\n\u001B[1;32m-> 4102\u001B[0m indexer \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcolumns\u001B[38;5;241m.\u001B[39mget_loc(key)\n\u001B[0;32m   4103\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_integer(indexer):\n\u001B[0;32m   4104\u001B[0m     indexer \u001B[38;5;241m=\u001B[39m [indexer]\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001B[0m, in \u001B[0;36mIndex.get_loc\u001B[1;34m(self, key)\u001B[0m\n\u001B[0;32m   3807\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(casted_key, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;129;01mor\u001B[39;00m (\n\u001B[0;32m   3808\u001B[0m         \u001B[38;5;28misinstance\u001B[39m(casted_key, abc\u001B[38;5;241m.\u001B[39mIterable)\n\u001B[0;32m   3809\u001B[0m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28many\u001B[39m(\u001B[38;5;28misinstance\u001B[39m(x, \u001B[38;5;28mslice\u001B[39m) \u001B[38;5;28;01mfor\u001B[39;00m x \u001B[38;5;129;01min\u001B[39;00m casted_key)\n\u001B[0;32m   3810\u001B[0m     ):\n\u001B[0;32m   3811\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m InvalidIndexError(key)\n\u001B[1;32m-> 3812\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01merr\u001B[39;00m\n\u001B[0;32m   3813\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[0;32m   3814\u001B[0m     \u001B[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001B[39;00m\n\u001B[0;32m   3815\u001B[0m     \u001B[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001B[39;00m\n\u001B[0;32m   3816\u001B[0m     \u001B[38;5;66;03m#  the TypeError.\u001B[39;00m\n\u001B[0;32m   3817\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_indexing_error(key)\n",
      "\u001B[1;31mKeyError\u001B[0m: ('UserInfo_2', 'UserInfo_4', 'UserInfo_7', 'UserInfo_8', 'UserInfo_9', 'UserInfo_19', 'UserInfo_20', 'UserInfo_22', 'UserInfo_24', 'Education_Info3')"
     ]
    }
   ],
   "execution_count": 85
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "#PCA 降维 \n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X_train) "
   ],
   "id": "976f4afccda08a53"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T13:14:32.148049Z",
     "start_time": "2024-09-26T13:14:31.879003Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.ensemble import RandomForestClassifier \n",
    "from sklearn.metrics import accuracy_score,roc_auc_score\n",
    "rf_model =  RandomForestClassifier(\n",
    "     n_estimators=100,\n",
    "     criterion=\"gini\",\n",
    "    max_depth=5, \n",
    "    random_state=0\n",
    ") \n",
    "rf_model.fit(X_train,y_train)"
   ],
   "id": "11fc1852b06f1537",
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: '赣州'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[83], line 9\u001B[0m\n\u001B[0;32m      2\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01msklearn\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mmetrics\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m accuracy_score,roc_auc_score\n\u001B[0;32m      3\u001B[0m rf_model \u001B[38;5;241m=\u001B[39m  RandomForestClassifier(\n\u001B[0;32m      4\u001B[0m      n_estimators\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m100\u001B[39m,\n\u001B[0;32m      5\u001B[0m      criterion\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgini\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m      6\u001B[0m     max_depth\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m5\u001B[39m, \n\u001B[0;32m      7\u001B[0m     random_state\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m\n\u001B[0;32m      8\u001B[0m ) \n\u001B[1;32m----> 9\u001B[0m rf_model\u001B[38;5;241m.\u001B[39mfit(X_train,y_train)\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\sklearn\\base.py:1473\u001B[0m, in \u001B[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001B[1;34m(estimator, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1466\u001B[0m     estimator\u001B[38;5;241m.\u001B[39m_validate_params()\n\u001B[0;32m   1468\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m config_context(\n\u001B[0;32m   1469\u001B[0m     skip_parameter_validation\u001B[38;5;241m=\u001B[39m(\n\u001B[0;32m   1470\u001B[0m         prefer_skip_nested_validation \u001B[38;5;129;01mor\u001B[39;00m global_skip_validation\n\u001B[0;32m   1471\u001B[0m     )\n\u001B[0;32m   1472\u001B[0m ):\n\u001B[1;32m-> 1473\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m fit_method(estimator, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\sklearn\\ensemble\\_forest.py:363\u001B[0m, in \u001B[0;36mBaseForest.fit\u001B[1;34m(self, X, y, sample_weight)\u001B[0m\n\u001B[0;32m    360\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m issparse(y):\n\u001B[0;32m    361\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124msparse multilabel-indicator for y is not supported.\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[1;32m--> 363\u001B[0m X, y \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_validate_data(\n\u001B[0;32m    364\u001B[0m     X,\n\u001B[0;32m    365\u001B[0m     y,\n\u001B[0;32m    366\u001B[0m     multi_output\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[0;32m    367\u001B[0m     accept_sparse\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcsc\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    368\u001B[0m     dtype\u001B[38;5;241m=\u001B[39mDTYPE,\n\u001B[0;32m    369\u001B[0m     force_all_finite\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[0;32m    370\u001B[0m )\n\u001B[0;32m    371\u001B[0m \u001B[38;5;66;03m# _compute_missing_values_in_feature_mask checks if X has missing values and\u001B[39;00m\n\u001B[0;32m    372\u001B[0m \u001B[38;5;66;03m# will raise an error if the underlying tree base estimator can't handle missing\u001B[39;00m\n\u001B[0;32m    373\u001B[0m \u001B[38;5;66;03m# values. Only the criterion is required to determine if the tree supports\u001B[39;00m\n\u001B[0;32m    374\u001B[0m \u001B[38;5;66;03m# missing values.\u001B[39;00m\n\u001B[0;32m    375\u001B[0m estimator \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mtype\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mestimator)(criterion\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcriterion)\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\sklearn\\base.py:650\u001B[0m, in \u001B[0;36mBaseEstimator._validate_data\u001B[1;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001B[0m\n\u001B[0;32m    648\u001B[0m         y \u001B[38;5;241m=\u001B[39m check_array(y, input_name\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124my\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mcheck_y_params)\n\u001B[0;32m    649\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 650\u001B[0m         X, y \u001B[38;5;241m=\u001B[39m check_X_y(X, y, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mcheck_params)\n\u001B[0;32m    651\u001B[0m     out \u001B[38;5;241m=\u001B[39m X, y\n\u001B[0;32m    653\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m no_val_X \u001B[38;5;129;01mand\u001B[39;00m check_params\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mensure_2d\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mTrue\u001B[39;00m):\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1273\u001B[0m, in \u001B[0;36mcheck_X_y\u001B[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001B[0m\n\u001B[0;32m   1268\u001B[0m         estimator_name \u001B[38;5;241m=\u001B[39m _check_estimator_name(estimator)\n\u001B[0;32m   1269\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m   1270\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mestimator_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m requires y to be passed, but the target y is None\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m   1271\u001B[0m     )\n\u001B[1;32m-> 1273\u001B[0m X \u001B[38;5;241m=\u001B[39m check_array(\n\u001B[0;32m   1274\u001B[0m     X,\n\u001B[0;32m   1275\u001B[0m     accept_sparse\u001B[38;5;241m=\u001B[39maccept_sparse,\n\u001B[0;32m   1276\u001B[0m     accept_large_sparse\u001B[38;5;241m=\u001B[39maccept_large_sparse,\n\u001B[0;32m   1277\u001B[0m     dtype\u001B[38;5;241m=\u001B[39mdtype,\n\u001B[0;32m   1278\u001B[0m     order\u001B[38;5;241m=\u001B[39morder,\n\u001B[0;32m   1279\u001B[0m     copy\u001B[38;5;241m=\u001B[39mcopy,\n\u001B[0;32m   1280\u001B[0m     force_all_finite\u001B[38;5;241m=\u001B[39mforce_all_finite,\n\u001B[0;32m   1281\u001B[0m     ensure_2d\u001B[38;5;241m=\u001B[39mensure_2d,\n\u001B[0;32m   1282\u001B[0m     allow_nd\u001B[38;5;241m=\u001B[39mallow_nd,\n\u001B[0;32m   1283\u001B[0m     ensure_min_samples\u001B[38;5;241m=\u001B[39mensure_min_samples,\n\u001B[0;32m   1284\u001B[0m     ensure_min_features\u001B[38;5;241m=\u001B[39mensure_min_features,\n\u001B[0;32m   1285\u001B[0m     estimator\u001B[38;5;241m=\u001B[39mestimator,\n\u001B[0;32m   1286\u001B[0m     input_name\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mX\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m   1287\u001B[0m )\n\u001B[0;32m   1289\u001B[0m y \u001B[38;5;241m=\u001B[39m _check_y(y, multi_output\u001B[38;5;241m=\u001B[39mmulti_output, y_numeric\u001B[38;5;241m=\u001B[39my_numeric, estimator\u001B[38;5;241m=\u001B[39mestimator)\n\u001B[0;32m   1291\u001B[0m check_consistent_length(X, y)\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\sklearn\\utils\\validation.py:1007\u001B[0m, in \u001B[0;36mcheck_array\u001B[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001B[0m\n\u001B[0;32m   1005\u001B[0m         array \u001B[38;5;241m=\u001B[39m xp\u001B[38;5;241m.\u001B[39mastype(array, dtype, copy\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m   1006\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m-> 1007\u001B[0m         array \u001B[38;5;241m=\u001B[39m _asarray_with_order(array, order\u001B[38;5;241m=\u001B[39morder, dtype\u001B[38;5;241m=\u001B[39mdtype, xp\u001B[38;5;241m=\u001B[39mxp)\n\u001B[0;32m   1008\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m ComplexWarning \u001B[38;5;28;01mas\u001B[39;00m complex_warning:\n\u001B[0;32m   1009\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m   1010\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mComplex data not supported\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(array)\n\u001B[0;32m   1011\u001B[0m     ) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mcomplex_warning\u001B[39;00m\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\sklearn\\utils\\_array_api.py:746\u001B[0m, in \u001B[0;36m_asarray_with_order\u001B[1;34m(array, dtype, order, copy, xp, device)\u001B[0m\n\u001B[0;32m    744\u001B[0m     array \u001B[38;5;241m=\u001B[39m numpy\u001B[38;5;241m.\u001B[39marray(array, order\u001B[38;5;241m=\u001B[39morder, dtype\u001B[38;5;241m=\u001B[39mdtype)\n\u001B[0;32m    745\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 746\u001B[0m     array \u001B[38;5;241m=\u001B[39m numpy\u001B[38;5;241m.\u001B[39masarray(array, order\u001B[38;5;241m=\u001B[39morder, dtype\u001B[38;5;241m=\u001B[39mdtype)\n\u001B[0;32m    748\u001B[0m \u001B[38;5;66;03m# At this point array is a NumPy ndarray. We convert it to an array\u001B[39;00m\n\u001B[0;32m    749\u001B[0m \u001B[38;5;66;03m# container that is consistent with the input's namespace.\u001B[39;00m\n\u001B[0;32m    750\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m xp\u001B[38;5;241m.\u001B[39masarray(array)\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:2153\u001B[0m, in \u001B[0;36mNDFrame.__array__\u001B[1;34m(self, dtype, copy)\u001B[0m\n\u001B[0;32m   2149\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__array__\u001B[39m(\n\u001B[0;32m   2150\u001B[0m     \u001B[38;5;28mself\u001B[39m, dtype: npt\u001B[38;5;241m.\u001B[39mDTypeLike \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m, copy: bool_t \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m   2151\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m np\u001B[38;5;241m.\u001B[39mndarray:\n\u001B[0;32m   2152\u001B[0m     values \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_values\n\u001B[1;32m-> 2153\u001B[0m     arr \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39masarray(values, dtype\u001B[38;5;241m=\u001B[39mdtype)\n\u001B[0;32m   2154\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m (\n\u001B[0;32m   2155\u001B[0m         astype_is_view(values\u001B[38;5;241m.\u001B[39mdtype, arr\u001B[38;5;241m.\u001B[39mdtype)\n\u001B[0;32m   2156\u001B[0m         \u001B[38;5;129;01mand\u001B[39;00m using_copy_on_write()\n\u001B[0;32m   2157\u001B[0m         \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_mgr\u001B[38;5;241m.\u001B[39mis_single_block\n\u001B[0;32m   2158\u001B[0m     ):\n\u001B[0;32m   2159\u001B[0m         \u001B[38;5;66;03m# Check if both conversions can be done without a copy\u001B[39;00m\n\u001B[0;32m   2160\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m astype_is_view(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdtypes\u001B[38;5;241m.\u001B[39miloc[\u001B[38;5;241m0\u001B[39m], values\u001B[38;5;241m.\u001B[39mdtype) \u001B[38;5;129;01mand\u001B[39;00m astype_is_view(\n\u001B[0;32m   2161\u001B[0m             values\u001B[38;5;241m.\u001B[39mdtype, arr\u001B[38;5;241m.\u001B[39mdtype\n\u001B[0;32m   2162\u001B[0m         ):\n",
      "\u001B[1;31mValueError\u001B[0m: could not convert string to float: '赣州'"
     ]
    }
   ],
   "execution_count": 83
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T13:14:27.872283Z",
     "start_time": "2024-09-26T13:14:27.848775Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print('accuracy：',accuracy_score(y_train,rf_model.predict(X_train)))\n",
    "print('roc_auc：',roc_auc_score(y_train,rf_model.predict_proba(y_train)[:,1]))"
   ],
   "id": "a371308711893979",
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'rf_model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[82], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124maccuracy：\u001B[39m\u001B[38;5;124m'\u001B[39m,accuracy_score(y_train,rf_model\u001B[38;5;241m.\u001B[39mpredict(X_train)))\n\u001B[0;32m      2\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mroc_auc：\u001B[39m\u001B[38;5;124m'\u001B[39m,roc_auc_score(y_train,rf_model\u001B[38;5;241m.\u001B[39mpredict_proba(y_train)[:,\u001B[38;5;241m1\u001B[39m]))\n",
      "\u001B[1;31mNameError\u001B[0m: name 'rf_model' is not defined"
     ]
    }
   ],
   "execution_count": 82
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T13:13:53.046214Z",
     "start_time": "2024-09-26T13:13:53.042405Z"
    }
   },
   "cell_type": "code",
   "source": "from lightgbm import LGBMClassifier \n",
   "id": "e7632a7a4ddfd44b",
   "outputs": [],
   "execution_count": 81
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T12:55:01.709930Z",
     "start_time": "2024-09-26T12:55:01.707280Z"
    }
   },
   "cell_type": "code",
   "source": [
    "lgb_model = LGBMClassifier(\n",
    "    num_leaves=10,\n",
    "    learning_rate=0.05,\n",
    "    n_estimators=1000,\n",
    "    subsample=0.8,\n",
    ")"
   ],
   "id": "5e70a0a21a8bb051",
   "outputs": [],
   "execution_count": 77
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-26T12:55:02.817987Z",
     "start_time": "2024-09-26T12:55:02.704417Z"
    }
   },
   "cell_type": "code",
   "source": [
    "lgb_model.fit(\n",
    "    X_train,y_train,\n",
    "    eval_metric='auc',\n",
    "    # verbose=50, \n",
    "    # early_stopping_rounds=30 #早停法，如果auc在30epoch没有进步就stop\n",
    ")"
   ],
   "id": "9d1d9041d5571be4",
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "pandas dtypes must be int, float or bool.\nFields with bad pandas dtypes: UserInfo_1: object, UserInfo_2: object, UserInfo_3: object, UserInfo_4: object, WeblogInfo_2: object, WeblogInfo_4: object, WeblogInfo_5: object, WeblogInfo_6: object, UserInfo_7: object, UserInfo_8: object, UserInfo_9: object, UserInfo_11: object, UserInfo_12: object, UserInfo_13: object, UserInfo_19: object, UserInfo_20: object, UserInfo_22: object, UserInfo_23: object, UserInfo_24: object, Education_Info2: object, Education_Info3: object, Education_Info4: object, Education_Info6: object, Education_Info7: object, Education_Info8: object, WeblogInfo_19: object, WeblogInfo_20: object, WeblogInfo_21: object, WeblogInfo_23: object, WeblogInfo_24: object, WeblogInfo_25: object, WeblogInfo_26: object, WeblogInfo_27: object, WeblogInfo_28: object, WeblogInfo_29: object, WeblogInfo_30: object, WeblogInfo_31: object, WeblogInfo_32: object, WeblogInfo_33: object, WeblogInfo_34: object, WeblogInfo_35: object, WeblogInfo_36: object, WeblogInfo_37: object, WeblogInfo_38: object, WeblogInfo_39: object, WeblogInfo_40: object, WeblogInfo_41: object, WeblogInfo_42: object, WeblogInfo_43: object, WeblogInfo_44: object, WeblogInfo_45: object, WeblogInfo_46: object, WeblogInfo_47: object, WeblogInfo_48: object, ListingInfo: object",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[78], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m lgb_model\u001B[38;5;241m.\u001B[39mfit(\n\u001B[0;32m      2\u001B[0m     X_train,y_train,\n\u001B[0;32m      3\u001B[0m     eval_metric\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mauc\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[0;32m      4\u001B[0m     \u001B[38;5;66;03m# verbose=50, \u001B[39;00m\n\u001B[0;32m      5\u001B[0m     \u001B[38;5;66;03m# early_stopping_rounds=30 #早停法，如果auc在30epoch没有进步就stop\u001B[39;00m\n\u001B[0;32m      6\u001B[0m )\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\lightgbm\\sklearn.py:1284\u001B[0m, in \u001B[0;36mLGBMClassifier.fit\u001B[1;34m(self, X, y, sample_weight, init_score, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_metric, feature_name, categorical_feature, callbacks, init_model)\u001B[0m\n\u001B[0;32m   1281\u001B[0m         \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m   1282\u001B[0m             valid_sets\u001B[38;5;241m.\u001B[39mappend((valid_x, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_le\u001B[38;5;241m.\u001B[39mtransform(valid_y)))\n\u001B[1;32m-> 1284\u001B[0m \u001B[38;5;28msuper\u001B[39m()\u001B[38;5;241m.\u001B[39mfit(\n\u001B[0;32m   1285\u001B[0m     X,\n\u001B[0;32m   1286\u001B[0m     _y,\n\u001B[0;32m   1287\u001B[0m     sample_weight\u001B[38;5;241m=\u001B[39msample_weight,\n\u001B[0;32m   1288\u001B[0m     init_score\u001B[38;5;241m=\u001B[39minit_score,\n\u001B[0;32m   1289\u001B[0m     eval_set\u001B[38;5;241m=\u001B[39mvalid_sets,\n\u001B[0;32m   1290\u001B[0m     eval_names\u001B[38;5;241m=\u001B[39meval_names,\n\u001B[0;32m   1291\u001B[0m     eval_sample_weight\u001B[38;5;241m=\u001B[39meval_sample_weight,\n\u001B[0;32m   1292\u001B[0m     eval_class_weight\u001B[38;5;241m=\u001B[39meval_class_weight,\n\u001B[0;32m   1293\u001B[0m     eval_init_score\u001B[38;5;241m=\u001B[39meval_init_score,\n\u001B[0;32m   1294\u001B[0m     eval_metric\u001B[38;5;241m=\u001B[39meval_metric,\n\u001B[0;32m   1295\u001B[0m     feature_name\u001B[38;5;241m=\u001B[39mfeature_name,\n\u001B[0;32m   1296\u001B[0m     categorical_feature\u001B[38;5;241m=\u001B[39mcategorical_feature,\n\u001B[0;32m   1297\u001B[0m     callbacks\u001B[38;5;241m=\u001B[39mcallbacks,\n\u001B[0;32m   1298\u001B[0m     init_model\u001B[38;5;241m=\u001B[39minit_model,\n\u001B[0;32m   1299\u001B[0m )\n\u001B[0;32m   1300\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\lightgbm\\sklearn.py:955\u001B[0m, in \u001B[0;36mLGBMModel.fit\u001B[1;34m(self, X, y, sample_weight, init_score, group, eval_set, eval_names, eval_sample_weight, eval_class_weight, eval_init_score, eval_group, eval_metric, feature_name, categorical_feature, callbacks, init_model)\u001B[0m\n\u001B[0;32m    952\u001B[0m evals_result: _EvalResultDict \u001B[38;5;241m=\u001B[39m {}\n\u001B[0;32m    953\u001B[0m callbacks\u001B[38;5;241m.\u001B[39mappend(record_evaluation(evals_result))\n\u001B[1;32m--> 955\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_Booster \u001B[38;5;241m=\u001B[39m train(\n\u001B[0;32m    956\u001B[0m     params\u001B[38;5;241m=\u001B[39mparams,\n\u001B[0;32m    957\u001B[0m     train_set\u001B[38;5;241m=\u001B[39mtrain_set,\n\u001B[0;32m    958\u001B[0m     num_boost_round\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mn_estimators,\n\u001B[0;32m    959\u001B[0m     valid_sets\u001B[38;5;241m=\u001B[39mvalid_sets,\n\u001B[0;32m    960\u001B[0m     valid_names\u001B[38;5;241m=\u001B[39meval_names,\n\u001B[0;32m    961\u001B[0m     feval\u001B[38;5;241m=\u001B[39meval_metrics_callable,  \u001B[38;5;66;03m# type: ignore[arg-type]\u001B[39;00m\n\u001B[0;32m    962\u001B[0m     init_model\u001B[38;5;241m=\u001B[39minit_model,\n\u001B[0;32m    963\u001B[0m     callbacks\u001B[38;5;241m=\u001B[39mcallbacks,\n\u001B[0;32m    964\u001B[0m )\n\u001B[0;32m    966\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_evals_result \u001B[38;5;241m=\u001B[39m evals_result\n\u001B[0;32m    967\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_best_iteration \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_Booster\u001B[38;5;241m.\u001B[39mbest_iteration\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\lightgbm\\engine.py:282\u001B[0m, in \u001B[0;36mtrain\u001B[1;34m(params, train_set, num_boost_round, valid_sets, valid_names, feval, init_model, feature_name, categorical_feature, keep_training_booster, callbacks)\u001B[0m\n\u001B[0;32m    280\u001B[0m \u001B[38;5;66;03m# construct booster\u001B[39;00m\n\u001B[0;32m    281\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 282\u001B[0m     booster \u001B[38;5;241m=\u001B[39m Booster(params\u001B[38;5;241m=\u001B[39mparams, train_set\u001B[38;5;241m=\u001B[39mtrain_set)\n\u001B[0;32m    283\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m is_valid_contain_train:\n\u001B[0;32m    284\u001B[0m         booster\u001B[38;5;241m.\u001B[39mset_train_data_name(train_data_name)\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\lightgbm\\basic.py:3637\u001B[0m, in \u001B[0;36mBooster.__init__\u001B[1;34m(self, params, train_set, model_file, model_str)\u001B[0m\n\u001B[0;32m   3630\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mset_network(\n\u001B[0;32m   3631\u001B[0m         machines\u001B[38;5;241m=\u001B[39mmachines,\n\u001B[0;32m   3632\u001B[0m         local_listen_port\u001B[38;5;241m=\u001B[39mparams[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlocal_listen_port\u001B[39m\u001B[38;5;124m\"\u001B[39m],\n\u001B[0;32m   3633\u001B[0m         listen_time_out\u001B[38;5;241m=\u001B[39mparams\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtime_out\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;241m120\u001B[39m),\n\u001B[0;32m   3634\u001B[0m         num_machines\u001B[38;5;241m=\u001B[39mparams[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnum_machines\u001B[39m\u001B[38;5;124m\"\u001B[39m],\n\u001B[0;32m   3635\u001B[0m     )\n\u001B[0;32m   3636\u001B[0m \u001B[38;5;66;03m# construct booster object\u001B[39;00m\n\u001B[1;32m-> 3637\u001B[0m train_set\u001B[38;5;241m.\u001B[39mconstruct()\n\u001B[0;32m   3638\u001B[0m \u001B[38;5;66;03m# copy the parameters from train_set\u001B[39;00m\n\u001B[0;32m   3639\u001B[0m params\u001B[38;5;241m.\u001B[39mupdate(train_set\u001B[38;5;241m.\u001B[39mget_params())\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\lightgbm\\basic.py:2576\u001B[0m, in \u001B[0;36mDataset.construct\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m   2571\u001B[0m             \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_set_init_score_by_predictor(\n\u001B[0;32m   2572\u001B[0m                 predictor\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_predictor, data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdata, used_indices\u001B[38;5;241m=\u001B[39mused_indices\n\u001B[0;32m   2573\u001B[0m             )\n\u001B[0;32m   2574\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m   2575\u001B[0m     \u001B[38;5;66;03m# create train\u001B[39;00m\n\u001B[1;32m-> 2576\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lazy_init(\n\u001B[0;32m   2577\u001B[0m         data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdata,\n\u001B[0;32m   2578\u001B[0m         label\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlabel,\n\u001B[0;32m   2579\u001B[0m         reference\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m   2580\u001B[0m         weight\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mweight,\n\u001B[0;32m   2581\u001B[0m         group\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mgroup,\n\u001B[0;32m   2582\u001B[0m         init_score\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39minit_score,\n\u001B[0;32m   2583\u001B[0m         predictor\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_predictor,\n\u001B[0;32m   2584\u001B[0m         feature_name\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfeature_name,\n\u001B[0;32m   2585\u001B[0m         categorical_feature\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcategorical_feature,\n\u001B[0;32m   2586\u001B[0m         params\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mparams,\n\u001B[0;32m   2587\u001B[0m         position\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mposition,\n\u001B[0;32m   2588\u001B[0m     )\n\u001B[0;32m   2589\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfree_raw_data:\n\u001B[0;32m   2590\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdata \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\lightgbm\\basic.py:2106\u001B[0m, in \u001B[0;36mDataset._lazy_init\u001B[1;34m(self, data, label, reference, weight, group, init_score, predictor, feature_name, categorical_feature, params, position)\u001B[0m\n\u001B[0;32m   2104\u001B[0m     categorical_feature \u001B[38;5;241m=\u001B[39m reference\u001B[38;5;241m.\u001B[39mcategorical_feature\n\u001B[0;32m   2105\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(data, pd_DataFrame):\n\u001B[1;32m-> 2106\u001B[0m     data, feature_name, categorical_feature, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpandas_categorical \u001B[38;5;241m=\u001B[39m _data_from_pandas(\n\u001B[0;32m   2107\u001B[0m         data\u001B[38;5;241m=\u001B[39mdata,\n\u001B[0;32m   2108\u001B[0m         feature_name\u001B[38;5;241m=\u001B[39mfeature_name,\n\u001B[0;32m   2109\u001B[0m         categorical_feature\u001B[38;5;241m=\u001B[39mcategorical_feature,\n\u001B[0;32m   2110\u001B[0m         pandas_categorical\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpandas_categorical,\n\u001B[0;32m   2111\u001B[0m     )\n\u001B[0;32m   2113\u001B[0m \u001B[38;5;66;03m# process for args\u001B[39;00m\n\u001B[0;32m   2114\u001B[0m params \u001B[38;5;241m=\u001B[39m {} \u001B[38;5;28;01mif\u001B[39;00m params \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;28;01melse\u001B[39;00m params\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\lightgbm\\basic.py:848\u001B[0m, in \u001B[0;36m_data_from_pandas\u001B[1;34m(data, feature_name, categorical_feature, pandas_categorical)\u001B[0m\n\u001B[0;32m    844\u001B[0m df_dtypes\u001B[38;5;241m.\u001B[39mappend(np\u001B[38;5;241m.\u001B[39mfloat32)\n\u001B[0;32m    845\u001B[0m target_dtype \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mresult_type(\u001B[38;5;241m*\u001B[39mdf_dtypes)\n\u001B[0;32m    847\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m (\n\u001B[1;32m--> 848\u001B[0m     _pandas_to_numpy(data, target_dtype\u001B[38;5;241m=\u001B[39mtarget_dtype),\n\u001B[0;32m    849\u001B[0m     feature_name,\n\u001B[0;32m    850\u001B[0m     categorical_feature,\n\u001B[0;32m    851\u001B[0m     pandas_categorical,\n\u001B[0;32m    852\u001B[0m )\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\lightgbm\\basic.py:794\u001B[0m, in \u001B[0;36m_pandas_to_numpy\u001B[1;34m(data, target_dtype)\u001B[0m\n\u001B[0;32m    790\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_pandas_to_numpy\u001B[39m(\n\u001B[0;32m    791\u001B[0m     data: pd_DataFrame,\n\u001B[0;32m    792\u001B[0m     target_dtype: \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnp.typing.DTypeLike\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m    793\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m np\u001B[38;5;241m.\u001B[39mndarray:\n\u001B[1;32m--> 794\u001B[0m     _check_for_bad_pandas_dtypes(data\u001B[38;5;241m.\u001B[39mdtypes)\n\u001B[0;32m    795\u001B[0m     \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m    796\u001B[0m         \u001B[38;5;66;03m# most common case (no nullable dtypes)\u001B[39;00m\n\u001B[0;32m    797\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m data\u001B[38;5;241m.\u001B[39mto_numpy(dtype\u001B[38;5;241m=\u001B[39mtarget_dtype, copy\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n",
      "File \u001B[1;32mD:\\Anaconda3\\Lib\\site-packages\\lightgbm\\basic.py:784\u001B[0m, in \u001B[0;36m_check_for_bad_pandas_dtypes\u001B[1;34m(pandas_dtypes_series)\u001B[0m\n\u001B[0;32m    778\u001B[0m bad_pandas_dtypes \u001B[38;5;241m=\u001B[39m [\n\u001B[0;32m    779\u001B[0m     \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mcolumn_name\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mpandas_dtype\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    780\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m column_name, pandas_dtype \u001B[38;5;129;01min\u001B[39;00m pandas_dtypes_series\u001B[38;5;241m.\u001B[39mitems()\n\u001B[0;32m    781\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m _is_allowed_numpy_dtype(pandas_dtype\u001B[38;5;241m.\u001B[39mtype)\n\u001B[0;32m    782\u001B[0m ]\n\u001B[0;32m    783\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m bad_pandas_dtypes:\n\u001B[1;32m--> 784\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m    785\u001B[0m         \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mpandas dtypes must be int, float or bool.\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m'\u001B[39m\n\u001B[0;32m    786\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mFields with bad pandas dtypes: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m, \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(bad_pandas_dtypes)\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m\n\u001B[0;32m    787\u001B[0m     )\n",
      "\u001B[1;31mValueError\u001B[0m: pandas dtypes must be int, float or bool.\nFields with bad pandas dtypes: UserInfo_1: object, UserInfo_2: object, UserInfo_3: object, UserInfo_4: object, WeblogInfo_2: object, WeblogInfo_4: object, WeblogInfo_5: object, WeblogInfo_6: object, UserInfo_7: object, UserInfo_8: object, UserInfo_9: object, UserInfo_11: object, UserInfo_12: object, UserInfo_13: object, UserInfo_19: object, UserInfo_20: object, UserInfo_22: object, UserInfo_23: object, UserInfo_24: object, Education_Info2: object, Education_Info3: object, Education_Info4: object, Education_Info6: object, Education_Info7: object, Education_Info8: object, WeblogInfo_19: object, WeblogInfo_20: object, WeblogInfo_21: object, WeblogInfo_23: object, WeblogInfo_24: object, WeblogInfo_25: object, WeblogInfo_26: object, WeblogInfo_27: object, WeblogInfo_28: object, WeblogInfo_29: object, WeblogInfo_30: object, WeblogInfo_31: object, WeblogInfo_32: object, WeblogInfo_33: object, WeblogInfo_34: object, WeblogInfo_35: object, WeblogInfo_36: object, WeblogInfo_37: object, WeblogInfo_38: object, WeblogInfo_39: object, WeblogInfo_40: object, WeblogInfo_41: object, WeblogInfo_42: object, WeblogInfo_43: object, WeblogInfo_44: object, WeblogInfo_45: object, WeblogInfo_46: object, WeblogInfo_47: object, WeblogInfo_48: object, ListingInfo: object"
     ]
    }
   ],
   "execution_count": 78
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "print('accuracy：',accuracy_score(y_train,lgb_model.predict(X_train)))\n",
    "print('roc_auc：',roc_auc_score(y_train,lgb_model.predict_proba(X_train)[:,1]))"
   ],
   "id": "dd83ec62800cc023"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
